Fitting ARIMA Models
Lecture 12
Assessing Predictive Performance
Test train split
The general approach is to keep the data ordered and split the first prop% into the training data and the remainder as testing data.
Model fit (training)
mm = rsample::training(co2_split) |>
model(
ARIMA(value~pdq(2,0,0) + PDQ(0,1,1, period=12)),
ARIMA(value~pdq(0,1,1) + PDQ(0,1,1, period=12)),
ARIMA(value),
ARIMA(value, stepwise = FALSE)
)
glance(mm)# A tibble: 4 × 8
.model sigma2 log_lik AIC AICc BIC ar_roots ma_roots
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <list> <list>
1 ARIMA(value ~ pdq(2, 0, 0)… 0.0865 -78.7 167. 167. 187. <cpl> <cpl>
2 ARIMA(value ~ pdq(0, 1, 1)… 0.0858 -76.8 160. 160. 172. <cpl> <cpl>
3 ARIMA(value) 0.0852 -73.8 160. 160. 184. <cpl> <cpl>
4 ARIMA(value, stepwise = FA… 0.0853 -73.7 161. 162. 189. <cpl> <cpl> Forecasting
Forecasting (1990-1996)
Accuracy
Out-of-sample:
# A tibble: 4 × 10
.model .type ME RMSE MAE MPE MAPE MASE RMSSE ACF1
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 ARIMA(value ~ pdq(0, 1,… Test 0.742 0.881 0.764 0.205 0.211 NaN NaN 0.684
2 ARIMA(value ~ pdq(2, 0,… Test 0.929 1.10 0.956 0.256 0.264 NaN NaN 0.761
3 ARIMA(value) Test 0.862 0.987 0.871 0.238 0.240 NaN NaN 0.686
4 ARIMA(value, stepwise =… Test 0.875 1.00 0.883 0.241 0.244 NaN NaN 0.689Within-sample:
# A tibble: 4 × 10
.model .type ME RMSE MAE MPE MAPE MASE RMSSE ACF1
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 ARIMA(value ~ pdq(… Trai… 0.0118 0.288 0.234 0.00340 0.0701 0.192 0.212 0.0122
2 ARIMA(value ~ pdq(… Trai… 0.0118 0.288 0.229 0.00349 0.0685 0.188 0.212 0.0288
3 ARIMA(value) Trai… 0.0144 0.286 0.227 0.00426 0.0680 0.187 0.210 0.0128
4 ARIMA(value, stepw… Trai… 0.0149 0.285 0.227 0.00440 0.0680 0.187 0.210 0.0113Rolling forecasting origin
::: {.aside} From Hyndman FPP3 - Chp 5.10 :::
One-step forecasts on test data
# A tibble: 4 × 10
.model .type ME RMSE MAE MPE MAPE MASE RMSSE ACF1
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 ARIMA(value ~ pdq… Trai… 0.135 0.322 0.294 0.0376 0.0815 0.179 0.186 0.0796
2 ARIMA(value ~ pdq… Trai… -0.0401 0.318 0.220 -0.0111 0.0608 0.134 0.184 0.0383
3 ARIMA(value) Trai… -0.0479 0.319 0.219 -0.0133 0.0606 0.133 0.185 0.0250
4 ARIMA(value, step… Trai… -0.0487 0.320 0.219 -0.0135 0.0606 0.133 0.185 0.0252Model Fitting
Fitting ARIMA
For an \(ARIMA(p,d,q)\) model,
Assumes that the data is stationary after differencing
Handling \(d\) is straight forward, just difference the original data \(d\) times (leaving \(n-d\) observations) \[ y'_t = \Delta^d \, y_t \]
After differencing, fit an \(ARMA(p,q)\) model to \(y'_t\).
To keep things simple we’ll assume \(w_t \overset{iid}{\sim} N(0,\sigma^2_w)\)
MLE - Stationarity & iid normal errors
If both of these assumptions are met, then the time series \(y_t\) will also be normal.
In general, the vector \(\boldsymbol{y} = (y_1, y_2, \ldots, y_t)'\) will have a multivariate normal distribution with mean \(\{\boldsymbol\mu\}_i = E(y_i) = E(y_t)\) and covariance \(\boldsymbol\Sigma\) where \(\{\boldsymbol{\Sigma}\}_{ij} = \gamma(i-j)\).
and therefore the joint density of y is given by
\[ f_{\boldsymbol y}(\boldsymbol y) = (2\pi)^{-t/2} \det(\boldsymbol\Sigma)^{-1/2} \times \exp\left( -\frac{1}{2}(\boldsymbol y - \boldsymbol \mu)'\Sigma^{-1}(\boldsymbol y - \boldsymbol \mu) \right) \]
AR
Fitting AR(1)
\[ y_t = \delta + \phi \, y_{t-1} + w_t \]
We need to estimate three parameters: \(\delta\), \(\phi\), and \(\sigma_w^2\), we know
\[ \begin{aligned} E(y_t) = \frac{\delta}{1-\phi} \quad&\quad Var(y_t) = \frac{\sigma_w^2}{1-\phi^2} \\ \gamma(h) &= \frac{\sigma_w^2}{1-\phi^2} \phi^{ | h | } \end{aligned} \]
Using these properties it is possible to write the distribution of \(y\) as a MVN but that does not make it easy to write down a (simplified) closed form for the MLE estimate for \(\delta\), \(\theta\), and \(\sigma_w^2\).
Conditional Density
We can also rewrite the density as follows,
\[ \begin{aligned} f(\boldsymbol y) &= f(y_t, y_{t-1}, \ldots, y_2, y_1) \\ &= f(y_t | y_{t-1}, \ldots, y_2, y_1) f(y_{t-1} | y_{t-2}, \ldots, y_2, y_1) \cdots f(y_2 | y_1) f(y_1) \\ &= f(y_t | y_{t-1}) f(y_{t-1} | y_{t-2}) \cdots f(y_2 | y_1) f(y_1) \end{aligned} \]
where,
\[ \begin{aligned} y_1 &\sim N\left(\delta,\frac{\sigma^2_w}{1-\phi^2} \right) \\ y_{t} | y_{t-1} &\sim N\left(\delta+\phi y_{t-1},\sigma^2_w \right) \\ f(y_{t} | y_{t-1}) &= \frac{1}{\sqrt{2\pi\sigma^2_w}} \exp \left( -\frac{1}{2}\frac{(y_t -\delta+\phi y_{t-1})^2 }{\sigma^2_w} \right) \end{aligned} \]
Log likelihood of AR(1)
\[ \log f(y_{t} | y_{t-1}) = -\frac{1}{2}\left( \log 2\pi + \log \sigma^2_w + \frac{1}{\sigma_w^2} (y_t -\delta+\phi y_{t-1})^2 \right) \]
\[ \begin{aligned} \ell(\delta, \phi, \sigma^2_w) &= \log f(\boldsymbol{y}) = \log f(y_1) + \sum_{i=2}^t \log f(y_{i} | y_{i-1}) \\ &= - \frac{1}{2} \bigg(\log 2\pi + \log \sigma_w^2 - \log (1-\phi^2) + \frac{(1-\phi^2)}{\sigma_w^2 }(y_1-\delta)^2 \bigg) \\ & ~~~~ - \frac{1}{2} \bigg( (n-1) \log 2\pi + (n-1) \log \sigma_w^2 + \frac{1}{\sigma_w^2} \sum_{i=2}^n (y_i -\delta+\phi y_{i-1})^2 \bigg) \\ &= - \frac{1}{2} \bigg( n \log 2\pi + n \log \sigma_w^2 - \log (1-\phi^2) \\ &~~~~~~~~~~~~~~~+ \frac{1}{\sigma_w^2} \bigg( (1-\phi^2)(y_1-\delta)^2 + \sum_{i=2}^n (y_i -\delta+\phi y_{i-1})^2 \bigg) \bigg) \end{aligned} \]
AR(1) Example
with \(\phi = 0.75\), \(\delta=0.5\), and \(\sigma_w^2=1\),
ARIMA
lm
Call:
lm(formula = value ~ lag(value), data = ar1)
Residuals:
Min 1Q Median 3Q Max
-2.7194 -0.6991 -0.0139 0.6323 3.3518
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.53138 0.07898 6.728 4.74e-11 ***
lag(value) 0.76141 0.02918 26.090 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.023 on 497 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.578, Adjusted R-squared: 0.5771
F-statistic: 680.7 on 1 and 497 DF, p-value: < 2.2e-16Bayesian AR(1) Model
library(brms) # must be loaded for arma to work
( ar1_brms = brm(value ~ arma(p = 1, q = 0), data=ar1, refresh=0) ) Family: gaussian
Links: mu = identity; sigma = identity
Formula: value ~ arma(p = 1, q = 0)
Data: ar1 (Number of observations: 500)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Correlation Structures:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
ar[1] 0.76 0.03 0.70 0.82 1.00 4257 3077
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 2.20 0.20 1.81 2.59 1.00 3474 2626
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 1.02 0.03 0.96 1.09 1.00 3445 2855
Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).brms Intercept vs δ?
The reported Intercept from the brms model is E(yt) and not δ - for an ARIMA(1,0,0) \[ E(y_t) = \frac{\delta}{1-\phi} ~~\Rightarrow~~ \delta = E(y_t) \* (1-\phi) \]
Chains
PP Checks
Posteriors
Predictions
Forecasting
Fitting AR(p)
Lagged Regression
As with the AR(1), we can rewrite the density using conditioning, \[ \begin{aligned} f(\boldsymbol y) &= f(y_t, y_{t-1}, \ldots, y_{2}, y_{1}) \\ &= f(y_{n} | y_{n-1},\ldots,y_{n-p}) \cdots f(y_{p+1} | y_p,\ldots,y_1) f(y_p, \ldots, y_1) \end{aligned} \]
Regressing \(y_t\) on \(y_{t-1}, \ldots, y_{t-p}\) gets us an approximate solution, but it ignores the \(f(y_1, \, y_2, \,\ldots, y_p)\) part of the likelihood.
How much does this matter (vs. using the full likelihood)?
If \(p\) is near to \(n\) then probably a lot
If \(p << n\) then probably not much
Method of Moments
Recall for an AR(p) process,
\[ \begin{aligned} \gamma(0) &= \sigma^2_w + \phi_1 \gamma(1) + \phi_2 \gamma(2) + \ldots + \phi_p \gamma(p) \\ \gamma(h) &= \phi_1 \gamma(h-1) + \phi_2 \gamma(h-2) + \ldots \phi_p \gamma(h-p) \end{aligned} \] We can rewrite the first equation in terms of \(\sigma^2_w\), \[ \sigma^2_w = \gamma(0) - \phi_1 \gamma(1) - \phi_2 \gamma(2) - \ldots - \phi_p \gamma(p) \] these are called the Yule-Walker equations.
Yule-Walker
These equations can be rewritten into matrix notation as follows
\[ \underset{p \times p}{\boldsymbol\Gamma_p} \underset{p \times 1}{\boldsymbol\phi} = \underset{p \times 1}{\boldsymbol\gamma_p} \qquad\qquad \underset{1 \times 1}{\sigma^2_w} = \underset{1 \times 1}{\gamma(0)} - \underset{1 \times p}{\boldsymbol{\phi\'}}\underset{p \times 1}{\boldsymbol{\gamma_p}} \]
where
\[ \begin{aligned} \underset{p \times p}{\boldsymbol{\Gamma_p}} &= \gamma(j-k)_{j,k} \\ \underset{p \times 1}{\boldsymbol\phi} &= (\phi_1, \phi_2, \ldots, \phi_p)\' \\ \underset{p \times 1}{\boldsymbol\gamma_p} &= (\gamma(1), \gamma(2), \ldots, \gamma(p))\' \end{aligned} \]
If we estimate the covariance structure from the data we obtain \(\hat{\boldsymbol\gamma_p}\) and \(\hat{\boldsymbol\Gamma_p}\) which we can plug in and solve for \(\boldsymbol{\phi}\) and \(\sigma^2_w\), \[ \hat{\boldsymbol\phi} =\hat{\boldsymbol{\Gamma}_p}^{-1}\hat{\boldsymbol{\gamma}_p} \qquad\qquad \hat{\sigma}^2_w = \gamma(0) - \hat{\boldsymbol{\gamma}_p}\' \hat{\boldsymbol{\Gamma}_p^{-1}} \hat{\boldsymbol{\gamma}_p} \]
Stan Code
// generated with brms 2.20.1
functions {
}
data {
int<lower=1> N; // total number of observations
vector[N] Y; // response variable
// data needed for ARMA correlations
int<lower=0> Kar; // AR order
int<lower=0> Kma; // MA order
// number of lags per observation
int<lower=0> J_lag[N];
int prior_only; // should the likelihood be ignored?
}
transformed data {
int max_lag = max(Kar, Kma);
}
parameters {
real Intercept; // temporary intercept for centered predictors
vector[Kar] ar; // autoregressive coefficients
real<lower=0> sigma; // dispersion parameter
}
transformed parameters {
real lprior = 0; // prior contributions to the log posterior
lprior += student_t_lpdf(Intercept | 3, 2.2, 2.5);
lprior += student_t_lpdf(sigma | 3, 0, 2.5)
- 1 * student_t_lccdf(0 | 3, 0, 2.5);
}
model {
// likelihood including constants
if (!prior_only) {
// matrix storing lagged residuals
matrix[N, max_lag] Err = rep_matrix(0, N, max_lag);
vector[N] err; // actual residuals
// initialize linear predictor term
vector[N] mu = rep_vector(0.0, N);
mu += Intercept;
// include ARMA terms
for (n in 1:N) {
err[n] = Y[n] - mu[n];
for (i in 1:J_lag[n]) {
Err[n + 1, i] = err[n + 1 - i];
}
mu[n] += Err[n, 1:Kar] * ar;
}
target += normal_lpdf(Y | mu, sigma);
}
// priors including constants
target += lprior;
}
generated quantities {
// actual population-level intercept
real b_Intercept = Intercept;
}ARMA
Fitting \(ARMA(2,2)\)
\[ y_t = \delta + \phi_1 \, y_{t-1} + \phi_2 \, y_{t-2} + \theta_1 w_{t-1} + \theta_2 w_{t-2} + w_t \]
We now need to estimate six parameters: \(\delta\), \(\phi_1\), \(\phi_2\), \(\theta_1\), \(\theta_2\) and \(\sigma_w^2\).
We could figure out \(E(y_t)\), \(Var(y_t)\), and \(Cov(y_t, y_{t+h})\), but the last two are going to be pretty nasty and the full MVN likehood is similarly going to be unpleasant to work with.
Like the AR(1) and AR(p) processes we want to use conditioning to simplify things.
\[ \begin{aligned} y_t | \delta, &y_{t-1}, y_{t-2}, w_{t-1}, w_{t-2} &\sim N(\delta + \phi_1y_{t-1} + \phi_2y_{t-2} + \theta_1 w_{t-1} + \theta_2 w_{t-2},~\sigma_w^2) \end{aligned} \]
ARMA(2,2) Example
with \(\phi = (0.75,-0.5)\), \(\theta = (0.5,0.2)\), \(\delta=0\), and \(\sigma_w^2=1\) using the same models
ARIMA
AR only lm
Call:
lm(formula = value ~ lag(value, 1) + lag(value, 2), data = d)
Residuals:
Min 1Q Median 3Q Max
-2.95562 -0.69955 0.00587 0.77063 3.13283
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.02892 0.04802 0.602 0.547
lag(value, 1) 1.07883 0.03430 31.455 <2e-16 ***
lag(value, 2) -0.64708 0.03438 -18.820 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.071 on 495 degrees of freedom
(2 observations deleted due to missingness)
Multiple R-squared: 0.6677, Adjusted R-squared: 0.6664
F-statistic: 497.4 on 2 and 495 DF, p-value: < 2.2e-16Hannan-Rissanen Algorithm
Estimate a high order AR (remember AR \(\Leftrightarrow\) MA when stationary + invertible)
Use AR to estimate values for unobserved \(w_t\) via
lmwithlagsRegress \(y_t\) onto \(y_{t-1}, \ldots, y_{t-p}, \hat{w}_{t-1}, \ldots \hat{w}_{t-q}\)
Update \(\hat{w}_{t-1}, \ldots \hat{w}_{t-q}\) based on current model,
Goto step 3, repeat until convergence
Hannan-Rissanen - Step 1 & 2
Series: value
Model: ARIMA(10,0,0)
Coefficients:
ar1 ar2 ar3 ar4 ar5
1.2167 -0.8380 0.0929 0.1696 -0.1273
s.e. 0.0446 0.0702 0.0796 0.0800 0.0807
ar6 ar7 ar8 ar9 ar10
0.0199 0.0275 -0.0839 0.1000 -0.0732
s.e. 0.0809 0.0809 0.0808 0.0713 0.0453
sigma^2 estimated as 1.089: log likelihood=-726.61
AIC=1475.22 AICc=1475.76 BIC=1521.58Residuals
Hannan-Rissanen - Step 3
d = mutate(d, w_hat = residuals(ar)$.resid)
(lm2 = lm(value ~ lag(value,1) + lag(value,2) + lag(w_hat,1) + lag(w_hat,2), data=d)) |>
summary()
Call:
lm(formula = value ~ lag(value, 1) + lag(value, 2) + lag(w_hat,
1) + lag(w_hat, 2), data = d)
Residuals:
Min 1Q Median 3Q Max
-2.95027 -0.67742 -0.06244 0.71858 2.75976
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.02114 0.04679 0.452 0.65165
lag(value, 1) 0.75166 0.07469 10.064 < 2e-16 ***
lag(value, 2) -0.50657 0.04264 -11.881 < 2e-16 ***
lag(w_hat, 1) 0.46618 0.08744 5.332 1.49e-07 ***
lag(w_hat, 2) 0.23440 0.08200 2.859 0.00443 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.043 on 493 degrees of freedom
(2 observations deleted due to missingness)
Multiple R-squared: 0.6861, Adjusted R-squared: 0.6836
F-statistic: 269.4 on 4 and 493 DF, p-value: < 2.2e-16Hannan-Rissanen - Step 4
d = mutate(d, w_hat = augment(lm2, newdata = d)$.resid)
(lm3 = lm(value ~ lag(value,1) + lag(value,2) + lag(w_hat,1) + lag(w_hat,2), data=d)) |>
summary()
Call:
lm(formula = value ~ lag(value, 1) + lag(value, 2) + lag(w_hat,
1) + lag(w_hat, 2), data = d)
Residuals:
Min 1Q Median 3Q Max
-2.94447 -0.66913 -0.05082 0.74799 2.82135
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.03373 0.04685 0.720 0.47181
lag(value, 1) 0.75139 0.07502 10.016 < 2e-16 ***
lag(value, 2) -0.50684 0.04272 -11.866 < 2e-16 ***
lag(w_hat, 1) 0.46361 0.08739 5.305 1.71e-07 ***
lag(w_hat, 2) 0.23576 0.08184 2.881 0.00414 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.042 on 491 degrees of freedom
(4 observations deleted due to missingness)
Multiple R-squared: 0.6877, Adjusted R-squared: 0.6851
F-statistic: 270.3 on 4 and 491 DF, p-value: < 2.2e-16Hannan-Rissanen - Step 3.2 + 4.2
d = mutate(d, w_hat = augment(lm3, newdata = d)$.resid)
(lm4 = lm(value ~ lag(value,1) + lag(value,2) + lag(w_hat,1) + lag(w_hat,2), data=d)) |>
summary()
Call:
lm(formula = value ~ lag(value, 1) + lag(value, 2) + lag(w_hat,
1) + lag(w_hat, 2), data = d)
Residuals:
Min 1Q Median 3Q Max
-2.97496 -0.67003 -0.04571 0.76249 2.78324
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.02735 0.04700 0.582 0.56093
lag(value, 1) 0.75600 0.07540 10.026 < 2e-16 ***
lag(value, 2) -0.50689 0.04287 -11.825 < 2e-16 ***
lag(w_hat, 1) 0.45663 0.08792 5.194 3.03e-07 ***
lag(w_hat, 2) 0.22510 0.08229 2.736 0.00645 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.044 on 489 degrees of freedom
(6 observations deleted due to missingness)
Multiple R-squared: 0.6845, Adjusted R-squared: 0.6819
F-statistic: 265.2 on 4 and 489 DF, p-value: < 2.2e-16Hannan-Rissanen - Step 3.3 + 4.3
d = mutate(d, w_hat = augment(lm4, newdata = d)$.resid)
(lm5 = lm(value ~ lag(value,1) + lag(value,2) + lag(w_hat,1) + lag(w_hat,2), data=d)) |>
summary()
Call:
lm(formula = value ~ lag(value, 1) + lag(value, 2) + lag(w_hat,
1) + lag(w_hat, 2), data = d)
Residuals:
Min 1Q Median 3Q Max
-2.95849 -0.66820 -0.03764 0.74071 2.77925
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.02909 0.04699 0.619 0.53617
lag(value, 1) 0.73934 0.07568 9.770 < 2e-16 ***
lag(value, 2) -0.50027 0.04291 -11.657 < 2e-16 ***
lag(w_hat, 1) 0.47620 0.08792 5.416 9.57e-08 ***
lag(w_hat, 2) 0.24336 0.08249 2.950 0.00333 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.042 on 487 degrees of freedom
(8 observations deleted due to missingness)
Multiple R-squared: 0.6859, Adjusted R-squared: 0.6833
F-statistic: 265.8 on 4 and 487 DF, p-value: < 2.2e-16Hannan-Rissanen - Step 3.4 + 4.4
d = mutate(d, w_hat = augment(lm5, newdata = d)$.resid)
(lm6 = lm(value ~ lag(value,1) + lag(value,2) + lag(w_hat,1) + lag(w_hat,2), data=d)) |>
summary()
Call:
lm(formula = value ~ lag(value, 1) + lag(value, 2) + lag(w_hat,
1) + lag(w_hat, 2), data = d)
Residuals:
Min 1Q Median 3Q Max
-2.96225 -0.68420 -0.04689 0.75628 2.77699
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.02844 0.04721 0.602 0.54718
lag(value, 1) 0.75148 0.07681 9.784 < 2e-16 ***
lag(value, 2) -0.50068 0.04335 -11.550 < 2e-16 ***
lag(w_hat, 1) 0.46357 0.08938 5.186 3.16e-07 ***
lag(w_hat, 2) 0.22960 0.08355 2.748 0.00622 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.044 on 485 degrees of freedom
(10 observations deleted due to missingness)
Multiple R-squared: 0.685, Adjusted R-squared: 0.6824
F-statistic: 263.6 on 4 and 485 DF, p-value: < 2.2e-16BRMS
Family: gaussian
Links: mu = identity; sigma = identity
Formula: value ~ arma(p = 2, q = 2) - 1
Data: d (Number of observations: 500)
Draws: 2 chains, each with iter = 5000; warmup = 2500; thin = 1;
total post-warmup draws = 5000
Correlation Structures:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
ar[1] 0.73 0.09 0.55 0.89 1.00 1864 2605
ar[2] -0.49 0.06 -0.60 -0.37 1.00 2205 3109
ma[1] 0.49 0.09 0.31 0.67 1.00 1884 2629
ma[2] 0.25 0.07 0.10 0.38 1.00 2150 2858
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 1.05 0.03 0.98 1.11 1.00 2811 2968
Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).Chains
Comparison
Predictions
Forecasting
Stan Code
// generated with brms 2.20.1
functions {
}
data {
int<lower=1> N; // total number of observations
vector[N] Y; // response variable
// data needed for ARMA correlations
int<lower=0> Kar; // AR order
int<lower=0> Kma; // MA order
// number of lags per observation
int<lower=0> J_lag[N];
int prior_only; // should the likelihood be ignored?
}
transformed data {
int max_lag = max(Kar, Kma);
}
parameters {
vector[Kar] ar; // autoregressive coefficients
vector[Kma] ma; // moving-average coefficients
real<lower=0> sigma; // dispersion parameter
}
transformed parameters {
real lprior = 0; // prior contributions to the log posterior
lprior += student_t_lpdf(sigma | 3, 0, 2.5)
- 1 * student_t_lccdf(0 | 3, 0, 2.5);
}
model {
// likelihood including constants
if (!prior_only) {
// matrix storing lagged residuals
matrix[N, max_lag] Err = rep_matrix(0, N, max_lag);
vector[N] err; // actual residuals
// initialize linear predictor term
vector[N] mu = rep_vector(0.0, N);
// include ARMA terms
for (n in 1:N) {
mu[n] += Err[n, 1:Kma] * ma;
err[n] = Y[n] - mu[n];
for (i in 1:J_lag[n]) {
Err[n + 1, i] = err[n + 1 - i];
}
mu[n] += Err[n, 1:Kar] * ar;
}
target += normal_lpdf(Y | mu, sigma);
}
// priors including constants
target += lprior;
}
generated quantities {
}Sta 344/644 - Fall 2023